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Biomedical Sensing and Wireless Technologies for Long Duration EVAs and Precursor Scout Missions

Dwight Day Xiongjie Dong William Kuhn

Don Gruenbacher Balasubramaniam Natarajan

Tim Sobering Mohammed Taj-Eldin

Steve Warren Kansas State University

Department of Electrical and Computer Engineering Manhattan, KS 66506

785-532-5600 ece@k-state.edu

Thomas BarstowRyan Broxterman

Kansas State University Department of Kinesiology

Manhattan, KS 66506 785-532-6765

kines@k-state.edu

Arlie Stonestreet II ICE Corporation

Manhattan, KS 66502 785-776-6423

Abstract— This paper summarizes a coordinated set of research efforts to support human and robotic missions to lunar, asteroid, and outer solar system destinations. Research areas include 1) selection and development of biosensors for astronauts engaged in long-duration, strenuous extra-vehicular activities, 2) sensor placement and operation inside spacesuits with minimal impact and maximum configurability using wireless links, 3) sensors powered using energy harvesting techniques and low-power network designs, and 4) application and maturation of UHF radio hardware to support these activities and the missions they enable.

TABLE OF CONTENTS

1. INTRODUCTION ................................................. 1 2. BIOMEDICAL SENSORS AND FATIGUE ASSESSMENT STUDIES .......................................... 2 3. INTRA-SUIT WIRELESS PROPAGATION ............ 4 4. ENERGY HARVESTING POWER ......................... 7 5. LOW POWER RADIOS AND NETWORKS ............ 8 6. UHF RADIO HARDWARE R&D ........................ 9 7. SUMMARY AND CONCLUSIONS ....................... 11 ACKNOWLEDGEMENTS ....................................... 11 REFERENCES ....................................................... 11 BIOGRAPHIES ...................................................... 12

1. INTRODUCTION

Astronauts involved in future missions to the moon, cislunar space, asteroids, and ultimately Mars and beyond, will face periods of strenuous activity during extravehicular activities (EVAs). Such activities may involve building of structures or conducting surface exploration of planetary bodies. To help maximize the work that can be accomplished, while simultaneously guarding astronaut safety by tracking and predicting health status, a wider set of biomedical monitoring devices is envisioned than those used today. To that end, the research presented here addresses sensor development, sensor selection, and methods to collect and upload data in real time while on-mission.

Current U.S. spacesuits monitor only electrocardiographic data and indirectly gauge oxygen uptake [1]. Given advances in commercial wireless electronics that operate at low power, many more sensors can be considered, provided they pose little or no burden to the astronauts who must wear them. Wearable biomedical devices investigated here include electrocardiographs, electromyographs, pulse oximeters, inductive plethysmographs, and accelerometers. Implementations of these sensors are elaborated in Section 2. Kinesiology studies involving sensors are also described, and the value of the electromyogram (EMG), which measures muscle activity, is highlighted as an approach to detect the onset of fatigue. EMG RMS values and median power frequencies are analyzed from several muscles during fatiguing and non-fatiguing exercise to determine the most useful signal parameters and most sensitive/informative sites to predict impending fatigue and/or task failure.

The spacesuit into which these sensors will need to be integrated is considered in Section 3. Although a number of new suit designs are being investigated, the Extravehicular Mobility Unit (EMU) manufactured by ILC Dover has served well for many years and remains the basis for many newer designs [2]. Hence, Section 3 focuses on outfitting spacesuits with sensors appropriate for the EMU design. To the extent that new suit designs continue to employ metalized layers for thermal insulation and micrometeoroid protection (thermal micrometeoroid garments (TMGs)), this study should also be applicable to newer designs. In our research, we have found that the current TMG suit layers create a radio-propagation environment that is well-modeled as a coaxial one in which the body itself plays the role of the coaxial center conductor [3]. Electromagnetic simulations and measurements using a full-scale suit model made with conductive fabric in the outer layers have been used to (a) quantify the radio path loss in this unique wireless link and (b) identify issues associated with the choice of radio frequencies and the affiliated antennas. The research indicates that UHF frequencies are generally the best choice, but intra-suit propagation path loss is sufficiently low from

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VHF through S-band frequencies to allow the use of radio transmitters in the milliwatt power range while maintaining at least 20 dB of link margin at data rates up to 1 Mbps.

Care is also needed when powering sensors that reside in a spacesuit. A spacesuit is an oxygen-rich environment, so it is critical to avoid sparks from high-energy sources such as batteries. One possible solution is to avoid batteries entirely and employ energy-harvesting techniques. This paper addresses energy harvesting and low-power radio network design in Section 4, including the practicality of operating biosensors and their associated radio transmitters using thermoelectric generators powered from the temperature difference between the body and the suit’s liquid cooling garment. Measurements to date suggest that up to 1 mW or more of average power generation may be possible at a sensor site using commercially available Peltier devices with areas of about 4 cm2. While this power may be adequate for continuous operation of the sensor electronics alone, the wireless link must focus on low-power digital signal processing and low-duty-cycle system operation. Fortunately, the link margins found in the studies covered in Section 3 suggest that it is possible to consider very low-complexity radio modems for intra-suit links, using sleep-wake cycles for low average power consumption. Section 5 outlines one such custom radio modem design being researched with this engineering tradeoff in mind.

Finally, Section 6 illustrates a hardware reference design that integrates a biosensor, energy harvester, radio transceiver, and antenna system. This integrated solution is comparable to the size of a wrist watch and requires no batteries or external power. Its radio is based on single-chip radio IC technologies researched by Kansas State University (KSU) under the Mars Technology Program [4] and operates in the UHF (400 MHz) band found to be the optimum choice for intra-suit propagation.

Beyond the spacesuit application, these technologies could be applied in areas like home healthcare [5], commercial MedRadio [6], or any area that may benefit from low power, low mass, and small volume envelopes. Research results are also being transferred to a Kansas aerospace company with the goal of producing a UHF radio adaptable to a variety of applications, including future small-sat and planetary probe missions. Section 6 concludes with an overview of this technology transfer effort.

2. BIOMEDICAL SENSORS AND FATIGUE ASSESSMENT STUDIES

A primary goal of this research is to create an in-suit sensor network that provides high-quality physiological data while maximizing movement and minimizing battery power. Subtasks are the selection, design, construction, and testing of a suite of wearable and in-suit medical sensors whose data can be uploaded from the suit to an external base station via a low-power radio communication link. Designs emphasize high-quality data, small size, low power

consumption, and enhanced mobility. Communication rates between the in-suit medical devices and the in-suit data logger will depend on the sensor types and the degree of preprocessing required. Overall, this work supports long-duration EVA planning for NASA's Human Research Program (HRP), where the goal of the HRP is to "provide human health and performance countermeasures, knowledge technologies, and tools to enable safe, reliable, and productive human space exploration"(see HRD-47052 [7]).

The research team has adopted a two-pronged approach to complete this work. The first set of activities targets field tests geared toward (a) assessing whether data from commercial off-the-shelf devices can be useful to identify and predict fatigue and (b) determining which of these sensing techniques and sites holds the most promise for in-suit applications. The second set of activities, informed by the field test results, focuses on the development of customized sensors and the supporting wired/wireless network for in-suit operation.

A. Field Activities

To evaluate the sensibility of using wearable sensor data to predict fatigue, the team has developed and utilized a collection of field tests in the form of an obstacle course, where individual tests mimic typical extravehicular activities. Tasks include lifting weighted boxes (Fig. 2.1 left panel), climbing ladders and stairs, and moving weighted objects in a wheelbarrow (Fig. 2.1 right panel) [8]. Off-the-shelf commercial sensors provide physiological monitoring for these tasks.

Figure 2.1 - Representative field tests performed by subjects wearing inspiration/expiration masks, electro-

myographs, and chest-worn electrocardiographs/ accelerometers.

A portable commercial gas exchange system (from Cosmed [9] in Fig. 2.1, currently from Oxycon [10]) provides measurement of whole body metabolic rate (oxygen uptake, carbon dioxide production, and ventilation) with sufficient breath-by-breath temporal resolution to track changes in metabolism during various tasks. Electromyograms and 3-axis accelerometer data (Delsys Trigno [11]) are acquired via discrete wireless sensors placed over 16 muscles/sites (Fig. 2.2, upper). A Zephyr BioHarness (Fig. 2.2, lower) provides heart rate and acceleration data from the chest. From this collective information, the best sites to predict muscle fatigue and task failure are selected for in-suit

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measurements. To date, 17 subjects have completed the obstacle courses under fatiguing and non-fatiguing conditions while wearing the sensors. Early results from these fatigue assessments were presented at recent NASA Human Research Program Investigators' Workshops [8, 12, 13] and other venues [14, 15].

Figure 2.2 - Electromyographic sensors on targeted muscle groups (upper). Zephyr BioHarness for heart

rate and activity monitoring (lower).

Acceleration data gathered during these experiments were also analyzed for their ability to indicate activity type, with the thought that these data might also provide a surrogate indicator for fatigue that does not require skin-contact electrodes. A representative confusion matrix from this work is depicted in Fig. 2.3 [16, 17] and indicates that simple accelerometer data can be a good resource for automatic task identification functions.

Figure 2.3 - Confusion matrix: accelerometer-based

activity identification.

More focused assessments of changes in forearm electromyogram signals (and their corresponding spectra) due to fatigue are being performed with the assistance of a custom hand-forearm ergometer, depicted in Fig. 2.4. Preliminary results archived in [18, 19] suggest that changes in the mean firing frequency assessed through short-time fast Fourier transform (FFT) analysis are well correlated with fatigue.

Figure 2.4 - Automated hand-forearm ergometer to assess forearm EMG data as a function of fatigue.

B. Biomedical Device Design and Assessment

In these studies, the team has affirmed the importance of • electromyograms from targeted muscle groups to

assess/predict fatigue, • inspiration/expiration masks for measurement of whole

body oxygen uptake, carbon dioxide production, and ventilation, and

• chest-worn accelerometers to quantify activity and identify activity type.

Additional sensor types can be embedded into a suit and perhaps even provide surrogate state-of-health information. Therefore, beyond migrating sensors with known benefit into a low-power, in-suit network, the team is customizing other sensors for this purpose [20]. Fig. 2.5 depicts some of the designs that have been, or are being, developed for this role. These wireless devices will form the nodes of an in-suit ZigBee Pro network that will (1) establish throughput requirements for a functional in-suit network and (2) serve as a performance baseline for future devices that employ ultra-low-power field-programmable gate arrays (FPGAs) and microtransceivers.

The six-axis accelerometer/gyroscope in Fig. 2.5, for example, will be used to assess acceleration at various body locations/joints. In terms of activity and fatigue identification, this approach may provide superior results relative to the chest-worn design utilized with the field tests described in the previous section (Fig. 2.3). Likewise, a reflectance pulse oximeter design [5, 21] will be customized to function effectively within either a helmet (e.g., for forehead measurements) or a glove (e.g., for wrist and finger measurements). The inductance plethysmograph in Fig. 2.5 uses a belt to obtain respiration rate, where the belt (typically worn around the chest) may be sewn into the upper portion of the cooling garment worn under the suit. The electrocardiograph in Fig. 2.5 is depicted in more detail in Fig. 2.6. This two channel system has been intentionally designed as a filter-free unit whose data can also potentially provide respiration rate and a metric related to ambient environmental noise.

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Figure 2.5 - ZigBee-enabled sensors, either completed or

under development for an in-suit network, that will be used as a power and performance baseline for upcoming

ultra-low-power designs.

Figure 2.6 - Two-channel electrocardiograph.

3. INTRA-SUIT WIRELESS PROPAGATION

Wireless links are desired to integrate these sensors into a spacesuit without excessive changes to the suit itself. While considerable work has been done on Body Area Networks (BANs) in the commercial world, that work is not easily applied to a spacesuit environment. Unlike radio transmission in typical wireless BAN environments, transmission within a spacesuit environment is strongly affected by the presence of aluminized mylar in the suit’s outer thermal micrometeoroid garment (TMG) layers shown in Fig. 3.1. At UHF frequencies and above, the propagation of radio waves is essentially contained within the suit, as with a waveguide structure or coaxial cable [3]. The goal of this task is to model and understand this special radio environment so that the necessary frequencies of operation and achievable data rates for low-power operation can be determined.

This task began by looking at an idealized small-scale model of the propagation problem of human body elements within metal tubes [3]. Under this condition, the signal is fully contained within the wave-guiding structure, with path losses resulting primarily from body absorption of the radio waves. A thorough literature review of related prior work was conducted. Our findings revealed that while the impact of the human body has been extensively studied in the context of wireless BANs, little work has been done to

understand propagation within a confined setting such as a spacesuit. We therefore developed an approximate analytical model for radio propagation within a spacesuit. A report by the IEEE working group on BANs [22] suggests that the human body behaves more like a conductor than a dielectric. As a result, we hypothesized and confirmed that the coaxial cable mode of propagation would be a better analytical fit than a waveguide model for in-suit propagation [3].

Figure 3.1 - Spacesuit layer stackup showing aluminized mylar in thermo-micrometeoroid outer cover layer, and

photo of materials from ILC Dover [23]. This small-scale model was examined using three methods: analytical predictions using coaxial computations (including series resistive loss terms), 3D EM modeling software, and models built and assessed in the laboratory. Examples of path loss results obtained for the small-scale arm model are reported in [3] where the environment was shown to be well modeled by a coaxial waveguide mechanism, with transverse electromagnetic (TEM) waves below about 700 MHz and multi-mode waves above that frequency. The current focus is on full-scale modeling, which is discussed and illustrated below.

A full-scale model of a suit incorporating a layer stack of fabrics and commercially available conductive materials

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was built in the KSU Apparel, Textiles and Interior Design department [24] and used by a team of KSU ECE students to measure propagation between various positions within the suit. The suit design and construction are illustrated in Fig. 3.2.

Figure 3.2 - Full-scale suit model construction.

Battery powered radios mated to KSU-designed top-hat antennas launch and receive the radio signals within the suit. Battery operation was found to be necessary to avoid errors from coupling and antenna effects between cables in a more traditional wired test setup. The antennas are shown in Figs. 3.3 and 3.4 prior to covering with the model-suit material. Antenna performance with and without the metal suit layers present is documented in [25]. While the top-hat antenna is not resonant, it has sufficient radiation resistance to provide useful excitation of transverse electromagnetic (TEM) coaxial fields in the suit-formed transmission-line structure. Mismatch losses are estimated at approximately 10 to 15 dB

at each end of the link, depending on frequency, with higher losses at lower frequencies.

To acquire real-time path-loss information in the presence of the fully RF-shielded model suit, received signal strength indicator (RSSI) voltages from the UHF radios inside the suit are converted into audio tones and observed on an oscilloscope, as illustrated in Fig. 3.5 [26].

Figure 3.3 - KSU-developed top-hat antenna/transducer

used for measurements.

Figure 3.4 - Radio / antenna combinations used for

measurements.

Figure 3.5 - Measuring signal strengths from the

receiver inside the radio-opaque suit using audio tones observed on an oscilloscope.

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Sensor-node locations (see Fig. 3.6.) were selected based on recommendations from researchers in the KSU Department of Kinesiology given the field tests discussed in Section 2. Those locations are found to be suitable for the purpose of astronaut health and activity monitoring [27].

Figure 3.6 - Sensor positions evaluated (from [26]).

Table 3.1 - Path loss measurement results for 315 MHz, 433 MHz and 916 MHz radios (from [26]).

Location 315 MHz

Path loss (dB)

433 MHz

Path loss (dB)

916 MHz

Path loss (dB)

TX RX

wrist (1) arm measurements (2) with 15 cm distance

13.3

11.6 21

wrist (1) arm measurements (3) with 30 cm distance

28.3 17.6 40

right ankle (8)

left ankle (10) 41.3 48.6 67.5

left wrist (14)

right wrist (1) 64.3 52.6 63

left wrist (14)

left ankle (10) 87.3 64.6 70

ankle (8) leg measurements (5)

26.3 22.6 37

ankle (8) leg measurements (6)

25.3 18.6 30

ankle (8) leg measurements (7)

25.3 15.6 25

shoulder (11)

chest (4) 27.3 16.6 44

mid upper arm (12)

chest (4) 37.3 28.6 49

mid lower arm (13)

chest (4) 40.3 29.6 57

mid calf (9)

chest (4) 47.3 40.6 56

right wrist (1)

chest (4) 44.3 35.6 60.5

Pure path loss values (with estimated antenna mismatch losses removed) are presented in Table 3.1 for 315 MHz, 433 MHz, and 916 MHz radio channels [26]. The highest loss values are for the left-wrist-to-left-ankle link (87 dB for 315 MHz, 65 dB for 433 MHz, and 70 dB for 916 MHz). This relatively high signal attenuation is due to the long propagation distance between the transmitter and receiver compared to other intra-suit links. As depicted in Fig. 3.6, this link requires the signal to make the transition between different body parts (e.g., arm-to-torso, torso-to-leg). These body parts have different impedances due their varying dielectric properties and tissue thicknesses, with the latter affecting center-conductor-to-shield gaps within each body part, which causes additional losses in the wireless signal.

A comparison between the measured path loss values for 315 MHz and 433 MHz radios indicates that losses for the 433 MHz radio channel are generally somewhat lower than those of the 315 MHz channel by about 7 to 10 dB. This observation is consistent with the results obtained in [3], but some experiments suggest the lower frequency should be on-par with the 433 MHz case. In practice, the expected challenge with operation at lower frequencies is developing a suitably sized antenna with good radiation efficiency.

When comparing the measured path loss for all three radio channels noted in Table 1, we conclude that the 916 MHz channel produces path losses that are significantly higher than those of the lower frequency channels. This is expected, since higher frequency channels suffer more attenuation in the intra-suit propagation environment [3].

Thus, the 400 MHz band is suggested as the favored frequency range of operation. This implies that the adjacent Medical Device Radiocommunications Service (MedRadio) band could be used for astronaut health and fatigue monitoring. With the conformal top-hat antenna developed and no adjustment for mismatch losses, received signal levels for the worst case sensor-to-chest path were on the order of -75 dBm at 433 MHz. This yields approximately 50 dB signal-to-noise ratios for 100 kbps BPSK or MSK data transmissions at 0 dBm transmit power. Thus, the link margin is on the order of 30 dB or higher. Propagation between the left wrist and left ankle yielded the worst overall path loss, but signals were still above –100 dBm in raw measurements for a 0dBm transmission, where path loss is not adjusted for antenna mismatch losses [26].

Current efforts are focused on studying the effects of breaks in the suit’s thermal micrometeoroid garment outer layer where different sections of the EMU suit join. Some signal leakage can be expected from these breaks and some modifications to the coaxial transmission-line signal propagation will result from impedance discontinuities. Preliminary measurements suggest these effects are minimal. However, future efforts will focus on tests within a real EMU suit to validate the results shown in Table 1.

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4. ENERGY HARVESTING POWER Biosensor electronics in a spacesuit should have minimal impact on both the astronaut and the suit. This favors small, lightweight sensors that are wireless and battery-free. Energy harvesting technology can offer battery-free operation, but significant constraints on average operating power are imposed. Fortunately, the experiments from Section 3 suggest that the radio link-budget should have high margins, even when operating in the 100 kbps data-rate range or above at an RF power of 1 mW or less. Hence, it should be possible to sample and collect low-to-medium-rate biosensor data over periods of seconds to minutes and then burst these data from a transmitter. If the burst time is sufficiently short due to the use of high-rate transmissions, and the modulation and demodulation techniques are sufficiently low-power, energy harvesting can become a practical power source for sensors and radios within the suit. This section examines practical issues with such operation.

Initial studies of possible energy harvesting mechanisms suitable for in-suit use identified several alternatives [25], including • thermoelectric devices harvesting from temperature

differences, • piezoelectric materials harvesting from force, and • mechanical motion-to-power converters. The focus was narrowed to thermoelectric generator (TEG) technology since it was determined to offer the most power availability within the suit environment.

Commercial sources of TEG technology suggest that achievable power levels range from microwatts to milliwatts depending on the surface area of the body covered, the location on the body, and the level of exercise [28]. While this technology is evolving and the milliwatt power levels are attractive, basic limits apply. The human body dissipates heat at a rate of approximately 2000 kilocalories per day at rest and somewhat more with activity. This equates to at least 100 watts over the surface area of the body. Hence, for a sensor limited to a small area comparable to that of a wrist-watch (about 15 cm2) on a person with a surface area of 1.5 m2, a maximum of 100 mW may be possible if the conversion efficiency is 100%.

To assess practical efficiencies and power outputs, measurements were made on Peltier devices, like the one depicted in Fig. 4.1, in an ideal setting. Such devices can operate in reverse-mode as thermo electric generators, but published datasheets do not provide information for this mode of operation. Hence, a collection of devices was sampled and tested.

When placing the device on a hotplate and holding the opposite side at room temperature with forced-air cooling, the open-circuit voltage was measured for 0° C to 20° C. The open-circuit voltage, Voc, and short-circuit current, Isc, were found to be largely linear versus ΔT, implying a quadratic power-versus-ΔT curve, assuming a constant fill

factor (FF) of 1 (P = FF*Voc*Isc). Maximum Voc values were 180 mV at 20 degrees differential, with associated Isc values of 70 to 200 mA, yielding power levels of up to 36 mW [25] (with FF = 1).

Figure 4.1 - Commercial Peltier device used as a thermo-

electric generator (TEG) in trials.

Subsequent measurements were performed by a team of students testing a model device attached to the body. The model device incorporated a TEG mounted between two copper plates measuring 5 x 6 cm2, with one contacting the skin and the other contacting a cooling garment. A commercial cooling jacket [29] was used as a stand-in for the liquid-cooling-garment of an actual spacesuit (see Fig. 4.2), and the jacket backpack was filled with ice and water, resulting in a relatively cold environment for the wearer. However, the jacket is designed for a thermal load similar to, or below that, found in an actual spacesuit, so the results are believed to be reasonable for a first-order estimate. Measured data in Table 4.1 indicate that power levels in the 1 to 2 mW range may be expected for the sensor hardware described in Section 5.

Figure 4.2 - Veskimo cooling jacket used to test energy-

harvesting performance.

Table 4.1 - Measured TEG output with and without the cooling jacket, using 30 cm2 contact plates.

Condition Power

(mW) With Cooling Vest Subject A Subject B

1.43 1.30

Without Cooling Vest (Free Air) Subject A Subject B

0.050 0.043

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5. LOW POWER RADIOS AND NETWORKS For biosensors to use energy harvesting sources, low-rate sensor data must be transferred at very low power. Much of the analog/RF microelectronics and high-performance modem research for such a radio had already been done at KSU and NASA JPL, respectively [4], but an ultra-low power digital design was needed for the current effort. A primary objective is to minimize the time that the radio is active, allowing the system to go into sleep mode most of the time. The process to attain the required low-power draw is illustrated in Fig. 5.1, which depicts the current drawn by the system as a function of time and task. The system has a very low sleep current, Is, which is drawn during the time S. However, when a sample is acquired, the microcontroller wakes up and acquires data, drawing current, Ids, for a time D. Once enough data have been collected, the system loads these data into a packet, starts the transmitter, and sends the data via the radio. From Fig. 5.1, the transmitter has by far the largest draw (on the order of 50 mW or more). Thus, the objective is to work toward as high of a data rate as possible, thereby minimizing the time that the transmitter is active. As a rough estimate, consider a 1 kbps overall sensor data rate. If the radio transmitter attains an instantaneous rate of 100 kbps, this would mean the radio needs to only be active 1 / 100 of the time.

Figure 5.1 - Current draw as a function of time and task.

A prototype system was designed, and two such systems were built. Each unit has a microcontroller, an FPGA, and basic radio hardware contained in a single RFIC. An image of a unit is shown in Fig. 5.2. The system has been used to prototype both a transmitter and receiver.

Figure 5.2 - Prototype system.

A high-level schematic of this prototype system is shown in Fig. 5.3. The energy-harvesting parts are on the left, the microcontroller is in the center, and the FPGA and radio hardware are on the right. The energy-harvesting system is illustrated in more detail in Section 6. The digital designs that go into the FPGA are of interest here.

Figure 5.3 - Energy-harvesting system schematic.

The transmitter was the first unit to be designed. A diagram of its major sections is shown in Fig. 5.4. The primary objective of the design is to move data from the micro-controller to the RFIC in an efficient manner. This was achieved by employing a First-In First-Out memory that allows the microcontroller to quickly send the data to the transmitter. Then, the transmit process is able to begin as soon as these data arrive. Also, the essential framing and error detection coding are done in hardware, reducing the overall time that the system needs to be active.

Figure 5.4 - Transmitter schematic.

The effectiveness of this approach is illustrated in Fig. 5.5. For this test, the transmitter code was loaded into the prototype and set to transmit a fixed message in a burst pattern similar to what is expected in application. The signals in the figure are as follows. The first signal, labeled “Detection Filter Output,” is the output from a detection filter applied to the output of the radio hardware. The signal will be low when no data are transmitted and switches between the two upper levels as different bits are transmitted. The transmitter becomes active once data are available, transmits these data, and then shuts down. The signals at the bottom of Fig. 5.5 are the various digital lines indicating data transmission from the microcontroller to the FPGA. Most notably, as soon as data arrive from the microcontroller, the FPGA begins transmission. When the transmission is complete, the “Transmission Done” signal is sent from the FPGA to the microcontroller, and the

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transmitter is shut down.

Figure 5.5 - Power sequencing during transmission.

The transmitter hardware also adds framing data to the transmission. This framing is laid out in Fig. 5.6, where the preamble allows for bit syncing, packet ID, packet type, packet size, data, and an error-detection CRC word. The packet structure is not a standard Ethernet type packet – a reduced set has been employed to decrease the time the transmitter is active.

Figure 5.6 - Data packet layout.

Fig. 5.7 illustrates the basic receiver blocks. The radio hardware was already available, as mentioned previously. The demodulator used for this project was chosen to be small and simple, taking advantage of the character of the network. Similarly, the clock reconstruction and frame detection were built as simply as possible.

Figure 5.7 - Receiver components.

The demodulator uses a phase frequency detector (PFD) common to phase-locked loops (PLLs). Due to the limited nature of the application, the design can simply use the PFD to compare the incoming signal with a reference clock generated from a numerically controlled oscillator (NCO).

Figure 5.8 - PFD demodulator.

Once the transmitter and receiver were designed, two prototypes were programmed as a transmitter and receiver,

respectively, and then connected via an adjustable attenuator. The system reliably transmitted a fixed message at attenuations of up to 90 dB. Note that the original objective data rate of 100 kbps has yet to be achieved. All testing was done at 10 kbps, which has proven reliable. Adapting the simplified modulator/ demodulator functions to higher rates is the focus of ongoing research.

6. UHF RADIO HARDWARE R&D The biosensor radio built at KSU for this research is a complex design involving the integration of the RFIC, an FPGA, a microcontroller, and a daughter board that incorporates a biomedical sensor, an energy-harvesting subsystem, and a radio antenna. Technologies to implement such radios are being researched in parallel with the sensor development in Section 2. In addition, some of the radio hardware research results are being investigated for use in unmanned scout missions.

A. In-Suit Radio Prototyping

The hardware developed for the main board is shown in Fig. 6.1, while the daughter board is shown in Fig. 6.2. The assembled electronics will reside in a plastic case (Fig. 6.3).

The main board is composed of KSU’s prototype UHF microtransceiver IC [4] (Fig. 6.1, lower left; labeled “5” in the photo), and a low-power Actel IGLOO FPGA (large IC to its right). The board also contains a temperature-compensated crystal oscillator above the RFIC and additional ICs handling voltage regulation and interfacing to programmers.

Figure 6.1 - Research radio main board sized for wrist

or ankle mounting.

The daughterboard in Fig. 6.2 contains three subsystems intended to support operation as a full biosensor radio within a space suit: 1. a top-hat antenna (see Section 3) to transmit and receive

signals, 2. a sensor or sensors to provide biomedical data (e.g.

electromyograms or accelerometer data) to the main board for transmission, and

3. an energy-harvesting power source for the main board using thermo-electric generator technology.

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The energy-harvesting electronics include an LTC3108 ultralow voltage step-up converter, a power management IC, and associated components capable of converting the 50 mV to 100 mV output of the TEG device to the 4 V level needed by the main board, with approximately 30% power efficiency. A super-capacitor for storage is also included on this daughter board to allow the main-board radio to draw up to 100 mA for short-burst transmissions while maintaining low overall power operation.

Figure 6.2 - Research radio daughter board containing

energy harvesting, 50 mF super-capacitor storage, sensors, and a radio transducer/antenna.

Both boards are configured to fit within a package (Fig. 6.3) that can be strapped to an astronaut’s wrist, ankle, or other location to collect the desired biomedical data and transmit it to a central radio (“gateway radio”) that interfaces to the main suit radio system. Based on measurements elaborated in Section 3, the gateway radio is envisioned to be mounted on the chest or back and hooked to the suit’s backpack and associated radio for relay to a ship or habitat-module computer.

Figure 6.3 - Main/daughter board set (upper) and

packaging created with a 3D printer (lower). Copper plates for heat conduction are not shown (see text).

Note that the case in Fig. 6.3 has an opening that will be filled with a copper plate to provide a thermal path from the

skin to the thermo-electric generator devices while also providing skin contact for the radio antenna. On the opposite side of this case, a second copper plate will contact the astronaut’s inner cooling garment and provide capacitive coupling to the aluminized mylar in the suit’s outer garment layers. This will complete the excitation of the radio wave coupling into the suit/arm coaxial structure. Hence, the top-hat “antenna” actually becomes a capacitive coupling transducer. The packaged radio will undergo testing beginning in Fall 2013, in association with the software described in Section 5, to form a reference baseline radio design. The intent is to validate the system and measured signal environment with an actual suit before the end of the project.

B. UHF Radio Assets for Scout Missions

While the radio hardware described above supports basic research, a companion task focuses on transitioning research results to commercial hardware for space and other high-reliability applications. Based on prior-work at KSU and NASA JPL during the Mars Technology Program [30], ICE Corporation [31] is working toward a low-mass, low-power, small-volume product that can be derived from the Mars Technology research results. This unit can accept RFICs based on KSU’s main radio and can support an optional custom-designed 1 watt companion power amplifier to form a medium-power UHF radio asset. The board includes an FPGA capable of supporting various modulation schemes, including those developed in prior work [4]. The production design is targeted for applications requiring a broad operational temperature range, radiation tolerance, and the ability to withstand severe shock and vibration. A photo of the prototype currently under development is shown in Fig. 6.4; it can be configured as a PCB assembly or custom package to form a stand-alone unit.

Figure 6.4 - PC board and package drawing for radio suitable for robotic scout-missions or cubesats.

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7. SUMMARY AND CONCLUSIONS Astronauts working on long-duration EVAs will encounter many challenges, some of which may lead to fatigue and suboptimal use of EVA time. By (a) researching the behavior of human musculature and other physiological systems and (b) developing biosensors for monitoring critical fatigue onset, mission scenarios can be adapted to optimize results while ensuring astronaut safety. These sensor data are envisioned to be collected from multiple locations inside a spacesuit and relayed to a base of operations using a low-power wireless network within the suit coupled to a backpack radio. The suit environment has been found to be best modeled as a lossy coaxial transmission system due to the outer metalized layers in the suit. Fortunately, measurements made on a medium-fidelity, full-scale-model suit suggest that radio link-budget margins are sufficiently high to allow operation from very low-power sources. Such sources include energy harvesting techniques such as thermoelectric generators running off of the temperature difference between the astronaut and the suit’s cooling garment. This avoids the need for batteries within the suit, increasing safety and simplifying logistics. A hardware prototype of the system is nearing completion and should serve as a reference design for future research and application. Many of the technologies being researched are broadly applicable to general low-power biosensor and radio and network applications, ranging from those envisioned for home healthcare to those needed on future robotic precursor scout-craft missions to outer planets and moons. To help begin transfer of the research results into products, a spin-off radio product is currently being designed by a commercial partner and is expected to be useful for high-reliability wireless links where custom reconfigurable radio assets are needed.

ACKNOWLEDGEMENTS The authors are indebted to NASA for funding this cooperative agreement work (NASA EPSCoR grant NNX11AM05A) and to a large team of students who have been instrumental in carrying it out. Graduate-student participants include Carl Ade, Ryan Broxterman, Matthew Clewell, Xiongjie Dong, Andy Fund, Dana Gude, Riley Harrington, Amy Hodges, Safa Khamis, Erin Monfort-Nelson, Shuo Ouyang, Susanna Schlup, Ian Sobering, Wen Song, Mohammed Taj-Eldin, Samuel Wilcox, and Chenyu Zheng. Undergraduate-student participants include Muhannad Alshetaiwi, Charles Carlson, Jordan Leiker, Guanting Liu, Brogan McWilliams, Yojana Mendoza, Garrett Peterson, Levi Riley, German Sanchez, and Jacob Sobering.

REFERENCES [1] NASA, “Putting on the EMU,” http://quest.nasa.gov/space/teachers/suited/5emu3.htm. [2] Ayrey, William. “ILC Space Suits & Related Products,

Rev. A,” http://www.hq.nasa.gov/alsj/ILC- SpaceSuits-RevA.pdf.

[3] Taj-Eldin, M., W. Kuhn and B. Natarajan. “Study of Radio Channel for Biomedical Sensors in Spacesuits,” 8th International Conference on Body Area Networks, Boston, MA, Sept. 30 - Oct. 2, 2013. In press.

[4] Kuhn, W., N. E. Lay, E. Grigorian, D. Nobbe, I. Kuperman, J. Jeon, K. Wong, Y. Tugnawat, and X. He. “A Microtransceiver for UHF Proximity Links Including Mars Surface-to-Orbit Applications,” Proceedings of the IEEE, Vol. 95, No. 10, pp. 2019-2044, Oct 2007.

[5] Li, Kejia and Steve Warren. “A Wireless Reflectance Pulse Oximeter Suitable for Wearable and Surface-Integratable Designs that Produces High-Quality Unfiltered Photoplethysmograms,” IEEE Transactions on Biomedical Circuits and Systems, Vol. 6, No. 3, June 2012, pp. 269–278.

[6] Federal Communications Commission, “Medical Device Radiocommunications Service (MedRadio),” http://www.fcc.gov/encyclopedia/medical-device-radiocommunications-service-medradio.

[7] NASA. “Human Research Program Requirements Document,” HRP-47052, Revision E, May 2011, http://www.nasa.gov/pdf/559800main_HRP-47052.pdf.

[8] Ade, C.J., R.M. Broxterman, S. Warren, R.D. Taylor, T.J. Barstow. “Development of Standardized Exercise Tests for Predicting Planetary Task Performance,” The International Academy of Astronautics Humans in Space Symposium, Houston, 2011.

[9] Cosmed K4b2 (Rome, Italy). [10] Oxycon Mobile (CareFusion, San Diego, CA) [11] Delsys Trigno (Natick, MA). [12] Broxterman, R.M., C.J. Ade, G.L. Gadbury, D.

Schinstock, S. Warren, and T.J. Barstow. “10-km Walkback Performance Predicted From Standardized Exercise Tests,” NASA Human Research Program Workshop, Houston, 2012.

[13] Broxterman, R.M., C. J Ade, S.L. Wilcox and T.J. Barstow. “Gender Differences in Laboratory Assessment and Simulated EVA Performance,” NASA Human Research Program Workshop, Galveston, 2013.

[14] Ade, C.J., R.M. Broxterman, G. L. Gadbury, D. Schinstock, S. Warren, and T.J. Barstow. “Physiological Responses During Simulated Planetary Field Tests,” American College of Sports Medicine, San Francisco, 2012.

[15] Wilcox, S.L., R.M. Broxterman, C.J. Ade, S.J. Schlulp, J.C. Craig, Y. Mendoza, L. Chavez, and T.J. Barstow. “The Relationship Between Physiologic Parameters in Upper Versus Lower Body Exercise,” American College of Sports Medicine, Indianapolis, 2013.

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[16] Song, Wen, Carl Ade, Ryan Broxterman, Thomas Nelson, Dana Gude, Thomas Barstow, and Steve Warren. “Classification Algorithms Applied to Accelerometer Data as a Means to Identify Subject Activities Related to Planetary Navigation Tasks,” HRP 2013, NASA Human Research Program Investigators’ Workshop, February 11–14, 2013, Moody Gardens Hotel, Galveston, TX.

[17] Song, Wen. Planetary Navigation Activity Recognition Using Wearable Accelerometer Data, Master’s thesis, Kansas State University, May 2013, http://krex.k-state.edu/.

[18] Gude, Dana, Ryan Broxterman, Carl Ade, Thomas Barstow, Thomas Nelson, Wen Song, and Steve Warren. “Automated Hand-Forearm Ergometer Data Collection System,” HRP 2013, NASA Human Research Program Investigators’ Workshop, February 11–14, 2013, Moody Gardens Hotel, Galveston, TX.

[19] Gude, Dana. Automated Hand-Forearm Ergometer Data Acquisition and Analysis System, Master’s thesis, Kansas State University, August 2013, http://krex.k-state.edu/.

[20] Dong, Xiongjie, Timothy Sobering, Thomas Barstow, and Steve Warren. “A Wireless Inductance Plethysmograph as a Precursor to a Networked Suite of Low-Power Sensors for In-Spacesuit Health Monitoring,” HRP 2013, NASA Human Research Program Investigators’ Workshop, February 11–14, 2013, Moody Gardens Hotel, Galveston, TX.

[21] Li, Kejia, Steve Warren, and Balasubramaniam Natarajan. “Onboard Tagging for Real-Time Quality Assessment of Photoplethysmograms Acquired by a Wireless Reflectance Pulse Oximeter,” IEEE Transactions on Biomedical Circuits and Systems, Vol. 6, No. 1, February 2012, pp. 54–63.

[22] Yazdandoost, K. Y. and K. Sayrafian-Pour. “Channel Model for Body Area Network (BAN),” IEEE P802.15 Working Group for Wireless Personal Area Networks (WPANs), April 2009.

https://mentor.ieee.org/802.15/dcn/08/15-08-0780-09-0006-tg6-channel-model.pdf .

[23] “Spacesuit,” The Worlds of David Darling, The Encyclopedia of Science,

http://www.daviddarling.info/images/extravehicular_mobility_unit.gif. Accessed Oct. 23, 2013.

[24] “Task 2- In Suit RF Propagation,” NASA at K-State, http://nasa.ece.ksu.edu/task2.html. Accessed Oct. 16, 2013.

[25] Hodges, Amelia L. Investigation of Antennas and Harvesting Methods for Use with a UHF Microtransceiver in a Biosensor Network, MS Thesis, Kansas State University, Manhattan, KS 66506, 2013, http://krex.k-state.edu/.

[26] M. Taj-Eldin, B. Kuhn, A. Hodges, B. Natarajan, G. Peterson, M. Alshetaiwi, S. Ouyang and G. Sanchez. “Wireless Propagation Measurements for Astronaut Body Area Network,” IEEE International Conference on Wireless for Space and Extreme Environments, Baltimore, MD, Nov. 7-9, 2013. In press.

[27] “Task 1- Medical Sensor Development,” Department of Electrical and Computer Engineering, Kansas State University, http://nasa.ece.ksu.edu/task1.html.

Accessed Oct, 22, 2013. [28] TEGwear Technology, http://www.perpetuapower.com/technology.htm. [29] Personal Cooling Jacket Product, http://www.veskimo.com/. [30] Antsos, Dimitrios. “Mars Technology Program

Communications and Tracking Technologies for Mars Exploration,” IEEE Aerospace Conference, 2006.

[31] ICE Corporation, http://www.ice-ks.com/.

BIOGRAPHIES Dwight Day received his BS, MS and Ph.D. in 1980, 1981 and 1987, respectively, all from Oklahoma State University and all in electrical engineering. Dwight worked for Texas Instruments, Inc. and Boeing Military Airplanes, in the area of image processing algorithms. Since

1990, Dwight has been a member of the faculty at Kansas State University (KSU), where he has been involved in a variety of image processing and signal processing research as well as a consultant in thefield of signal processing applications.

Xiongjie Dong received a B.S. degree in Electrical Engineering from Kansas State University, Manhattan, KS in 2011, where he is currently pursuing an M.S. degree in Electrical Engineering. He is a research assistant in the KSU

Medical Component Device Laboratory, and his research interests include medical device hardware design, wireless biosensor networks, and statistical signal processing.

Don Gruenbacher is an Associate Professor and Head of the Kansas State University Department of Electrical and Computer Engineering. During his career at K-State, Don has chaired and served on various committees at the department, college, and university level. He has been recognized as

an outstanding faculty member by both Eta Kappa Nu and Mortar Board. His research activities are focused in the areas of computer networks, communications, and digital design. Prior to joining K-State EECE as a faculty member, Gruenbacher was a member of the senior staff in the Space Department of the Johns Hopkins University Applied Physics Laboratory between 1994-1997 and 1989-1990. He received a bachelor's in electrical engineering in 1989, a master's degree in 1991, and a doctorate in 1994, all from K-State.

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William Kuhn received the Ph.D. degree from Virginia Tech in 1996. From 1979 to 1981, he was with Ford Aerospace and Communications Corporation, Palo Alto, CA, designing satellite receiver equipment. From 1983 to 1992, he was with the Georgia Tech Research Institute, Atlanta, performing radar

analysis and developing analog/digital circuit simulators and models. In 1996, he joined Kansas State University, Manhattan. He teaches courses in circuit design, communications theory, radio and microwave circuit/system design, and VLSI. His research is targeted at low-power radio electronics in CMOS, BiCMOS, GaAs, and SOI technologies and has ranged from characterization of spiral inductors to the design of full radio transceivers. He is also involved in system-design research including propagation studies and energy harvesting for low-power radio implementation.

Balasubramaniam Natarajan received his B.E and Ph.D. degrees, both in electrical engineering from Birla Institute of Technology and Science, Pilani, India and Colorado State University, Fort Collins, CO, in 1997 and 2002, respectively. Since fall 2002, he has been a faculty member in the Department of Electrical and

Computer Engineering at Kansas State University, where he is currently the Clair N Palmer and Sara M Palmer Professor and Director of the WiCom (Wireless Communication) and Information Processing Research Group. His research interests include spread spectrum communications, multi-carrier CDMA and OFDM, multi-user detection, cognitive radio networks, sensor signal processing, distributed detection and estimation, and antenna array processing.

Dr. Natarajan has over 125 journal and refereed conference publications in the wireless communications and signal processing arena, has published a book entitled Multi-carrier Technologies for Wireless Communications (Kluwer Academic Publishers, 2002) and holds a patent on customized spreading sequence design algorithm for CDMA systems.

Tim Sobering received B.S. (1982) and M.S. (1984) degrees in Electrical Engineering from Kansas State University. From 1984 to 1996 he was at Sandia National Laboratories involved in the development of ground-based, airborne, and space-based remote sensing instruments used in

monitoring compliance with nuclear, chemical, and biological arms control treaties. In 1996 he joined Kansas State University as Director of the Electronics Design Laboratory (EDL), a multidisciplinary core laboratory created to support the research and economic development missions at Kansas Regents Institutions. Sobering has developed considerable expertise in the design of precision, low-noise, analog electronics associated with various sensing technologies. He is currently active in the design of neutron sensors for security applications. Sobering leverages his design experience by teaching courses in the Department of Electrical and Computer Engineering at K-State.

Mohammed TajEldin received his B.S in Informatics Engineering from University of Aleppo, Syria and M.S degree in Electrical Engineering from Florida Institute of Technology, Florida in 2011. Mohammed is working now towards his PhD degree in Electrical

Engineering with the Department of Electrical and Computer Engineering, Kansas State University. His research interests include body area networks, wireless channel characterization and performance evaluation, and wearable antennas.

Steve Warren received a B.S. and M.S. in Electrical Engineering from Kansas State University in 1989 and 1991, respectively, followed by a Ph.D. in Electrical Engineering from The University of Texas at Austin in 1994. Dr. Warren is an Associate Professor in the Department of Electrical & Computer Engineering

at Kansas State University. Prior to joining KSU in August 1999, Dr. Warren was a Principal Member of the Technical Staff at Sandia National Laboratories in Albuquerque, NM. He directs the KSU Medical Component Design Laboratory, a facility partially funded by the National Science Foundation that provides resources for the research and development of distributed medical monitoring technologies and learning tools that support biomedical contexts. Dr. Warren is a member of the American Society for Engineering Education and the Institute of Electrical and Electronics Engineers.

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Thomas J. Barstow received his B.S. (Nutrition Science, 1974), M.A. (Physical Education, 1978) and Ph.D. (Physiology, 1985) from U.C. Davis. Dr. Barstow is Professor in the Kinesiology Department at Kansas State University, where he joined the faculty in 1996. Prior to that, he was a postdoctoral fellow, then

adjunct assistant professor, at Harbor-UCLA Medical Center, in the Department of Medicine, Respiratory Division. He is co-director of the Human Exercise Physiology Laboratory at KSU. His research foci include: 1) determinants of muscle fatigue during exercise, 2) interactions of blood flow with muscle contractions, 3) endothelial function, and 4) physical conditioning requirements necessary for astronauts to successfully perform mission-related tasks. Dr. Barstow is a member of the American Physiological Society, and is a Fellow in the American College of Sports Medicine.

Ryan M. Broxterman is currently a student at Kansas State University working toward a Ph.D. degree in Anatomy and Physiology. He earned a M.S. degree in Kinesiology from Kansas State University in 2011 and a B.A.S. degree in Physical Education from Washburn University in 2009. His research is targeted at

understanding the mechanisms of muscular fatigue and the limitations of human activity in areas such as patient ambulation, exercise, and spaceflight.

Arlie Stonestreet II obtained a BSEE from Kansas State University in 1995 and is Chief Design Engineer at ICE Corporation, an AS9100 registered company specializing in custom design, manufacture, and test of complex electronic controllers for aircraft

and aerospace applications. He holds multiple US and international patents and enjoys analog and mixed signal design challenges.